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Ye C, Ho R, Moberg KH, Zheng JQ. Sexual Dimorphism in Age-Dependent Neurodegeneration After Mild Head Trauma in Drosophila : Unveiling the Adverse Impact of Female Reproductive Signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583747. [PMID: 38496515 PMCID: PMC10942469 DOI: 10.1101/2024.03.06.583747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Environmental insults, including mild head trauma, significantly increase the risk of neurodegeneration. However, it remains challenging to establish a causative connection between early-life exposure to mild head trauma and late-life emergence of neurodegenerative deficits, nor do we know how sex and age compound the outcome. Using a Drosophila model, we demonstrate that exposure to mild head trauma causes neurodegenerative conditions that emerge late in life and disproportionately affect females. Increasing age-at-injury further exacerbates this effect in a sexually dimorphic manner. We further identify Sex Peptide (SP) signaling as a key factor in female susceptibility to post-injury brain deficits. RNA sequencing highlights a reduction in innate immune defense transcripts specifically in mated females during late life. Our findings establish a causal relationship between early head trauma and late-life neurodegeneration, emphasizing sex differences in injury response and the impact of age-at-injury. Finally, our findings reveal that reproductive signaling adversely impacts female response to mild head insults and elevates vulnerability to late-life neurodegeneration.
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Qiu H, Zador Z, Lannon M, Farrokhyar F, Duda T, Sharma S. Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis. Sci Rep 2024; 14:1294. [PMID: 38221527 PMCID: PMC10788338 DOI: 10.1038/s41598-024-51474-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 01/05/2024] [Indexed: 01/16/2024] Open
Abstract
Traumatic brain injury (TBI) is a complex condition where heterogeneity impedes the advancement of care. Understanding the diverse presentations of TBI is crucial for personalized medicine. Our study aimed to identify clinically relevant patient endotypes in TBI using latent class analysis based on comorbidity data. We used the Medical Information Mart for Intensive Care III database, which includes 2,629 adult TBI patients. We identified five stable endotypes characterized by specific comorbidity profiles: Heart Failure and Arrhythmia, Healthy, Renal Failure with Hypertension, Alcohol Abuse, and Hypertension. Each endotype had distinct clinical characteristics and outcomes: The Heart Failure and Arrhythmia endotype had lower survival rates than the Renal Failure with Hypertension despite featuring fewer comorbidities overall. Patients in the Hypertension endotype had higher rates of neurosurgical intervention but shorter stays in contrast to the Alcohol Abuse endotype which had lower rates of neurosurgical intervention but significantly longer hospital stays. Both endotypes had high overall survival rates comparable to the Healthy endotype. Logistic regression models showed that endotypes improved the predictability of survival compared to individual comorbidities alone. This study validates clinical endotypes as an approach to addressing heterogeneity in TBI and demonstrates the potential of this methodology in other complex conditions.
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Affiliation(s)
- Hongbo Qiu
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
| | - Zsolt Zador
- Division of Neurosurgery, McMaster University, Hamilton, ON, Canada
| | - Melissa Lannon
- Division of Neurosurgery, McMaster University, Hamilton, ON, Canada
| | - Forough Farrokhyar
- Department of Health, Evidence and Impact, McMaster University, Hamilton, ON, Canada
| | - Taylor Duda
- Division of Neurosurgery, McMaster University, Hamilton, ON, Canada
| | - Sunjay Sharma
- Division of Neurosurgery, McMaster University, Hamilton, ON, Canada
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3
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Radabaugh HL, Ferguson AR, Bramlett HM, Dietrich WD. Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation. Neurotherapeutics 2023; 20:1433-1445. [PMID: 37525025 PMCID: PMC10684440 DOI: 10.1007/s13311-023-01400-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2023] [Indexed: 08/02/2023] Open
Abstract
The use of animal models in pre-clinical research has significantly broadened our understanding of the pathologies that underlie traumatic brain injury (TBI)-induced damage and deficits. However, despite numerous pre-clinical studies reporting the identification of promising neurotherapeutics, translation of these therapies to clinical application has so far eluded the TBI research field. A concerted effort to address this lack of translatability is long overdue. Given the inherent heterogeneity of TBI and the replication crisis that continues to plague biomedical research, this is a complex task that will require a multifaceted approach centered around rigor and reproducibility. Here, we discuss the role of three primary focus areas for better aligning pre-clinical research with clinical TBI management. These focus areas are (1) reporting and standardization of protocols, (2) replication of prior knowledge including the confirmation of expected pharmacodynamics, and (3) the broad application of open science through inter-center collaboration and data sharing. We further discuss current efforts that are establishing the core framework needed for successfully addressing the translatability crisis of TBI.
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Affiliation(s)
- Hannah L Radabaugh
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Helen M Bramlett
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
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4
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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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5
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Feld SI, Hippe DS, Miljacic L, Polissar NL, Newman SF, Nair BG, Vavilala MS. A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury. J Neurosurg Anesthesiol 2023; 35:215-223. [PMID: 34759236 PMCID: PMC9091057 DOI: 10.1097/ana.0000000000000819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/08/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Traumatic brain injury (TBI) is a major cause of death and disability. Episodes of hypotension are associated with worse TBI outcomes. Our aim was to model the real-time risk of intraoperative hypotension in TBI patients, compare machine learning and traditional modeling techniques, and identify key contributory features from the patient monitor and medical record for the prediction of intraoperative hypotension. METHODS The data included neurosurgical procedures in 1005 TBI patients at an academic level 1 trauma center. The clinical event was intraoperative hypotension, defined as mean arterial pressure <65 mm Hg for 5 or more consecutive minutes. Two types of models were developed: one based on preoperative patient-level predictors and one based on intraoperative predictors measured per minute. For each of these models, we took 2 approaches to predict the occurrence of a hypotensive event: a logistic regression model and a gradient boosting tree model. RESULTS The area under the receiver operating characteristic curve for the intraoperative logistic regression model was 0.80 (95% confidence interval [CI]: 0.78-0.83), and for the gradient boosting model was 0.83 (95% CI: 0.81-0.85). The area under the precision-recall curve for the intraoperative logistic regression model was 0.16 (95% CI: 0.12-0.20), and for the gradient boosting model was 0.19 (95% CI: 0.14-0.24). Model performance based on preoperative predictors was poor. Features derived from the recent trend of mean arterial pressure emerged as dominantly predictive in both intraoperative models. CONCLUSIONS This study developed a model for real-time prediction of intraoperative hypotension in TBI patients, which can use computationally efficient machine learning techniques and a streamlined feature-set derived from patient monitor data.
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Affiliation(s)
- Shara I Feld
- Anesthesiology and Pain Medicine, University of Washington
| | - Daniel S Hippe
- The Mountain-Whisper-Light: Statistics & Data Science, Seattle, WA
| | | | - Nayak L Polissar
- The Mountain-Whisper-Light: Statistics & Data Science, Seattle, WA
| | | | - Bala G Nair
- Anesthesiology and Pain Medicine, University of Washington
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6
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Chou A, Torres-Espín A, Huie JR, Krukowski K, Lee S, Nolan A, Guglielmetti C, Hawkins BE, Chaumeil MM, Manley GT, Beattie MS, Bresnahan JC, Martone ME, Grethe JS, Rosi S, Ferguson AR. Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research. Neurotrauma Rep 2022; 3:139-157. [PMID: 35403104 PMCID: PMC8985540 DOI: 10.1089/neur.2021.0061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.
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Affiliation(s)
- Austin Chou
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Abel Torres-Espín
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - J Russell Huie
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
| | - Karen Krukowski
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
| | - Sangmi Lee
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Amber Nolan
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
| | - Caroline Guglielmetti
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Bridget E Hawkins
- Department of Anesthesiology, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
- Moody Project for Traumatic Brain Injury Research, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
| | - Myriam M Chaumeil
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Geoffrey T Manley
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Michael S Beattie
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
| | - Jacqueline C Bresnahan
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
| | - Maryann E Martone
- Department of Neuroscience, University of California San Diego, San Diego, California, USA
| | - Jeffrey S Grethe
- Department of Neuroscience, University of California San Diego, San Diego, California, USA
| | - Susanna Rosi
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
- Kavli Institute of Fundamental Neuroscience, University of California San Francisco, San Francisco, California, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
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7
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Suarez JI. Big Data/AI in Neurocritical Care: Maybe/Summary. Neurocrit Care 2021; 37:166-169. [PMID: 34966957 DOI: 10.1007/s12028-021-01422-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022]
Abstract
Big data (BD) and artificial intelligence (AI) have increasingly been used in neurocritical care. "BD" can be operationally defined as extremely large datasets that are so large and complex that they cannot be analyzed by using traditional statistical modeling. "AI" means the ability of machines to perform tasks similar to those performed by human intelligence. We present a brief overview of the most commonly applied AI techniques to perform BD analytics and discuss some of the recent promising examples in the field of neurocritical care. The latter include the following: cognitive motor dissociation in disorders of consciousness, hypoxic-ischemic injury following cardiac arrest, delayed cerebral ischemia and vasospasm after subarachnoid hemorrhage, and monitoring of intracranial pressure. It is imperative that we develop multicenter collaborations to tackle BD. These collaborations will allow us to share data, combine predictive algorithms, and analyze multiple and cumulative sources of data retrospectively and prospectively. Once AI algorithms are validated at multiple centers, they should be tested in randomized controlled trials investigating their impact on clinical outcome. The neurocritical care community must work to ensure that AI incorporates standards to ensure fairness and health equity rather than reflect our biases present in our collective conscience.
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Affiliation(s)
- Jose I Suarez
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine, Neurology, and Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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8
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Nourelahi M, Dadboud F, Khalili H, Niakan A, Parsaei H. A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months. Acute Crit Care 2021; 37:45-52. [PMID: 34762793 PMCID: PMC8918709 DOI: 10.4266/acc.2021.00486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/28/2021] [Indexed: 11/30/2022] Open
Abstract
Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcome in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. In this study, we examined the capability of a machine learning-based model in predicting "favorable" or "unfavorable" outcome after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, accuracy, and area under the curve (AUC). Ten-fold cross-validation method was used to estimate these indices. Overall, the developed models showed excellent performance with AUC >0.81, sensitivity and specificity of > 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are "GCS motor response," "pupillary reactivity," and "age." Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set.
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Affiliation(s)
- Mehdi Nourelahi
- Department of Computer Science, University of Wyoming, Laramie, WY, USA
| | - Fardad Dadboud
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hosseinali Khalili
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amin Niakan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hossein Parsaei
- Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.,Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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9
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Maleki N, Finkel A, Cai G, Ross A, Moore RD, Feng X, Androulakis XM. Post-traumatic Headache and Mild Traumatic Brain Injury: Brain Networks and Connectivity. Curr Pain Headache Rep 2021; 25:20. [PMID: 33674899 DOI: 10.1007/s11916-020-00935-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/29/2020] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW Post-traumatic headache (PTH) consequent to mild traumatic brain injury (mTBI) is a complex, multidimensional, chronic neurological disorder. The purpose of this review is to evaluate the current neuroimaging studies on mTBI and PTH with a specific focus on brain networks and connectivity patterns. RECENT FINDINGS We present findings on PTH incidence and prevalence, as well as the latest neuroimaging research findings on mTBI and PTH. Additionally, we propose a new strategy in studying PTH following mTBI. The diversity and heterogeneity of pathophysiological mechanisms underlying mild traumatic brain injury pose unique challenges on how we interpret neuroimaging findings in PTH. Evaluating alterations in the intrinsic brain network connectivity patterns using novel imaging and analytical techniques may provide additional insights into PTH disease state and therefore inform effective treatment strategies.
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Affiliation(s)
- Nasim Maleki
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Alan Finkel
- Carolina Headache Institute, 6114 Fayetteville Rd, Suite 109, Durham, NC, USA
| | - Guoshuai Cai
- Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Alexandra Ross
- University of South Carolina School of Medicine, Columbia, SC, 29209, USA
| | - R Davis Moore
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Xuesheng Feng
- Navy Region Mid-Atlantic, Reserve Component Command, 1683 Gilbert Street, Norfolk, VA, 23511, USA
| | - X Michelle Androulakis
- University of South Carolina School of Medicine, Columbia, SC, 29209, USA. .,Columbia VA Health Care System, Columbia, SC, 20208, USA.
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10
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Vivaldi N, Caiola M, Solarana K, Ye M. Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification. IEEE Trans Biomed Eng 2021; 68:3205-3216. [PMID: 33635785 PMCID: PMC9513823 DOI: 10.1109/tbme.2021.3062502] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Objectives: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (TBI) history from those with stroke history and/or normal EEG. Methods: Support vector machine (SVM) and K-nearest neighbors (KNN) models were generated with a diverse feature set from Temple EEG Corpus for both two-class classification of patients with TBI history from normal subjects and three-class classification of TBI, stroke and normal subjects. Results: For two-class classification, an accuracy of 0.94 was achieved in 10-fold cross validation (CV), and 0.76 in independent validation (IV). For three-class classification, 0.85 and 0.71 accuracy were reached in CV and IV respectively. Overall, linear discriminant analysis (LDA) feature selection and SVM models consistently performed well in both CV and IV and for both two-class and three-class classification. Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. But stroke patients showed a greater degree of change and had additional global decrease in theta power. Conclusions: Our study suggests that EEG data-driven machine learning can be a useful tool for TBI classification. Significance: Our study provides preliminary evidence that EEG ML algorithm can potentially provide specificity to separate different neurological conditions.
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11
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Radabaugh H, Bonnell J, Schwartz O, Sarkar D, Dietrich WD, Bramlett HM. Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy. J Neurotrauma 2021; 38:1670-1678. [PMID: 33107380 DOI: 10.1089/neu.2020.7357] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML's feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set. As a proof-of-concept, we aimed to better evaluate the multi-variate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a pre-processed data set that incorporated metrics from four feature groups to determine their ability to correctly identify specific drug treatments. In all but one of the possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers, the exception being nicotinamide versus amantadine. Further, when the same methods were employed to assess different doses of the same treatment, the ML classifiers had greater difficulty in understanding which treatment each sample received. Our data serve as a critical first step toward identifying optimal treatments for specific subgroups of samples that are dependent on factors such as types and severity of traumatic injuries, as well as informing the prediction of therapeutic combinations that may lead to greater treatment effects than individual therapies.
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Affiliation(s)
- Hannah Radabaugh
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Jerry Bonnell
- Department of Computer Science, University of Miami College of Arts and Sciences, Miami, Florida, USA
| | - Odelia Schwartz
- Department of Computer Science, University of Miami College of Arts and Sciences, Miami, Florida, USA
| | - Dilip Sarkar
- Department of Computer Science, University of Miami College of Arts and Sciences, Miami, Florida, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Helen M Bramlett
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA.,Bruce W. Carter Department of Veterans Affairs Medical Center, Miami, Florida, USA
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12
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Race and Concussion: An Emerging Relationship. Ochsner J 2021; 20:348-349. [PMID: 33408569 PMCID: PMC7755552 DOI: 10.31486/toj.20.0145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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13
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Khan IH, Javaid M. Big Data Applications in Medical Field: A Literature Review. JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT 2020. [DOI: 10.1142/s242486222030001x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.
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Affiliation(s)
- Ibrahim Haleem Khan
- School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
| | - Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
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Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17030748. [PMID: 31991582 PMCID: PMC7037379 DOI: 10.3390/ijerph17030748] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/13/2020] [Accepted: 01/16/2020] [Indexed: 12/15/2022]
Abstract
Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), there are also challenges including lack of information about how patient progress observed in the outpatient clinic translates into improved functional performance at home. At present, outpatient providers must rely on patient-reported information about functional progress (or lack thereof) at home and in the community. Information and communication technologies (ICT) offer another option—data collected about the patient’s adherence, performance and progress made on home exercises could be used to help guide course corrections between clinic visits, enhancing effectiveness and efficiency of outpatient care. In this article, we describe our efforts to explore use of sensor-enhanced home exercise and big data analytics in medical rehabilitation. The goal of this work is to demonstrate how sensor-enhanced exercise can improve rehabilitation outcomes for patients with significant neurological impairment (e.g., from stroke, traumatic brain injury, and spinal cord injury). We provide an overview of big data analysis and explain how it may be used to optimize outpatient rehabilitation, creating a more efficient model of care. We describe our planned development efforts to build advanced analytic tools to guide home-based rehabilitation and our proposed randomized trial to evaluate effectiveness and implementation of this approach.
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15
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Wu S, Zhao W, Rowson B, Rowson S, Ji S. A network-based response feature matrix as a brain injury metric. Biomech Model Mechanobiol 2019; 19:927-942. [PMID: 31760600 DOI: 10.1007/s10237-019-01261-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 11/11/2019] [Indexed: 01/06/2023]
Abstract
Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based "response feature matrix" to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metric is illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validation are conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targeted multi-scale modeling in the future.
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Affiliation(s)
- Shaoju Wu
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA
| | - Bethany Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Steven Rowson
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.
- Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
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Agoston DV, Vink R, Helmy A, Risling M, Nelson D, Prins M. How to Translate Time: The Temporal Aspects of Rodent and Human Pathobiological Processes in Traumatic Brain Injury. J Neurotrauma 2019; 36:1724-1737. [PMID: 30628544 PMCID: PMC7643768 DOI: 10.1089/neu.2018.6261] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Traumatic brain injury (TBI) triggers multiple pathobiological responses with differing onsets, magnitudes, and durations. Identifying the therapeutic window of individual pathologies is critical for successful pharmacological treatment. Dozens of experimental pharmacotherapies have been successfully tested in rodent models, yet all of them (to date) have failed in clinical trials. The differing time scales of rodent and human biological and pathological processes may have contributed to these failures. We compared rodent versus human time scales of TBI-induced changes in cerebral glucose metabolism, inflammatory processes, axonal integrity, and water homeostasis based on published data. We found that the trajectories of these pathologies run on different timescales in the two species, and it appears that there is no universal "conversion rate" between rodent and human pathophysiological processes. For example, the inflammatory process appears to have an abbreviated time scale in rodents versus humans relative to cerebral glucose metabolism or axonal pathologies. Limitations toward determining conversion rates for various pathobiological processes include the use of differing outcome measures in experimental and clinical TBI studies and the rarity of longitudinal studies. In order to better translate time and close the translational gap, we suggest 1) using clinically relevant outcome measures, primarily in vivo imaging and blood-based proteomics, in experimental TBI studies and 2) collecting data at multiple post-injury time points with a frequency exceeding the expected information content by two or three times. Combined with a big data approach, we believe these measures will facilitate the translation of promising experimental treatments into clinical use.
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Affiliation(s)
- Denes V. Agoston
- Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, Maryland
| | - Robert Vink
- Division of Health Science, University of South Australia, Adelaide, Australia
| | - Adel Helmy
- Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Mårten Risling
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - David Nelson
- Department of Physiology and Pharmacology, Section of Perioperative Medicine and Intensive Care, Karolinska Institutet, Stockholm, Sweden
| | - Mayumi Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, California
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Sullivan KA, Kaye SA, Blaine H, Edmed SL, Meares S, Rossa K, Haden C. Psychological approaches for the management of persistent postconcussion symptoms after mild traumatic brain injury: a systematic review. Disabil Rehabil 2019; 42:2243-2251. [DOI: 10.1080/09638288.2018.1558292] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Karen A. Sullivan
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Sherrie-Anne Kaye
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Hannah Blaine
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Shannon L. Edmed
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Susanne Meares
- Department of Psychology, Macquarie University, Sydney, Australia
- Brain Injury Rehabilitation Services, Westmead Hospital, Sydney, Australia
| | - Kalina Rossa
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Catherine Haden
- Library, Queensland University of Technology, Brisbane, Australia
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